Automatic scaling of at least one user application to external clouds

ABSTRACT

Embodiments of the invention provide a method, a system and a computer program product configured to automatically auto-scale a user compute instance to multiple cloud providers while considering a multiplicity of user requirements. The method, executed on a digital data processor, includes obtaining information, via a user interface, that is descriptive of user cloud computing related preferences, including a user cloud computing budgetary preference. The method further includes sensing properties of a plurality of clouds and making decisions, based at least on the obtained information and on the sensed properties, of when to scale up or scale down the user cloud instance, of selecting one of the plurality of clouds as where to scale the user cloud instance, and determining which resource or resources of the selected cloud to add or remove from the selected cloud. The method further includes automatically executing the decisions on the selected cloud.

TECHNICAL FIELD

The various embodiments of this invention relate generally to dataprocessing systems, methods and computer software and, morespecifically, relate to systems, methods and computer software wherein auser's application and associated workload can be instantiated onto avirtual data processing platform such as a public cloud.

BACKGROUND

Cloud computing is an evolving data processing technique. Cloudcomputing has been described in a number of documents including, forexample, a document published by the National Institute of Standards andTechnology (NIST), “The NIST Definition of Cloud Computing”, Peter Mell,Timothy Grance, NIST Special Publication 800-145, September 2011.

So-called automatic scaling or “auto-scaling” allows users to scaletheir virtual data processing instances automatically. At least onecloud service provider offers a capability to users via a web service tolaunch or terminate compute instances. Scaling involves adding orremoving servers in an infrastructure environment (horizontal) or mayinclude adding or removing resources to an existing server (vertical).

However, existing approaches to accomplish auto-scaling consider onlyone cloud environment. These existing approaches thus limit andconstrain the elasticity of the cloud concept.

SUMMARY

The embodiments of this invention provide a method, a system and acomputer program product configured to automatically auto-scale a usercompute instance to multiple cloud providers while considering amultiplicity of user requirements.

The method, executed on a digital data processor, includes obtaininginformation, via a user interface, that is descriptive of user cloudcomputing related preferences, including a user cloud computingbudgetary preference. The method further includes sensing properties ofa plurality of clouds; making decisions, based at least on the obtainedinformation and on the sensed properties of the plurality of clouds, ofwhen to scale up or scale down the user cloud instance, of selecting oneof the plurality of clouds as where to scale the user cloud instance,and determining which resource or resources of the selected cloud to addor remove from the selected cloud. The method further includesautomatically executing the decisions on the selected cloud.

The system is comprised of at least one data processor connected with atleast one memory that stores software instructions, where execution ofthe software instructions by the at least one data processor causes thesystem to obtain information, via a user interface, that is descriptiveof user cloud computing related preferences, including a user cloudcomputing budgetary preference; sense properties of a plurality ofclouds; and make decisions, based at least on the obtained informationand on the sensed properties of the plurality of clouds, of when toscale up or scale down a user cloud instance, of selecting one of theplurality of clouds as where to scale the user cloud instance, anddetermining which resource or resources of the selected cloud to add orremove from the selected cloud. The system is further configured toautomatically execute the decisions on the selected cloud.

The computer program product is comprised of software instructions on acomputer-readable medium. Execution of the software instructions using acomputer results in performing operations that comprise obtaininginformation, via a user interface, that is descriptive of user cloudcomputing related preferences, including a user cloud computingbudgetary preference; sensing properties of a plurality of clouds; andmaking decisions, based at least on the obtained information and on thesensed properties of the plurality of clouds, of when to scale up orscale down a user cloud instance, of selecting one of the plurality ofclouds as where to scale the user cloud instance, and determining whichresource or resources of the selected cloud to add or remove from theselected cloud. Execution of the software instructions further resultsin automatically executing the decisions on the selected cloud.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 shows a simplified system block diagram that is useful whendiscussing a general flow of a method in accordance with embodiments ofthis invention and the operation of a multi-cloud auto-scaling engine inaccordance with embodiments of this invention.

FIG. 2 shows in greater detail a user interface sub-system that forms apart of the multi-cloud auto-scaling engine shown in FIG. 1.

FIG. 3 shows in greater detail a cloud sensor and monitor sub-systemthat forms a part of the multi-cloud auto-scaling engine shown in FIG.1.

FIG. 4 shows in greater detail a decision sub-system that forms a partof the multi-cloud auto-scaling engine shown in FIG. 1.

FIG. 5 shows one non-limiting example of a data processing systemsuitable for hosting the multi-cloud auto-scaling engine shown in FIGS.1-4.

FIG. 6 illustrates a logic flow diagram (also referred to as a flowchart) in accordance with an example of a non-limiting embodiment ofthis invention.

FIG. 7 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 8 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 9 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

It is understood in advance that although this disclosure includes adetailed description related at least in part to cloud computing,implementation of the teachings recited herein are not limited to acloud computing environment. Rather, embodiments of the presentinvention are capable of being implemented in conjunction with any othertype of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email).

The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications.

The consumer does not manage or control the underlying cloudinfrastructure but has control over operating systems, storage, deployedapplications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and can be owned by an organizationselling cloud services.

The public cloud can be shared amongst its users, where the users maybelong to different public groups, companies or organizations.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forloadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring first now to FIG. 7 there is shown a schematic of an exampleof a cloud computing node 10. Cloud computing node 10 is only oneexample of a suitable cloud computing node and is not intended tosuggest any limitation as to the scope of use or functionality ofembodiments of the invention described herein. Regardless, cloudcomputing node 10 is capable of being implemented and/or performing anyof the functionality set forth below.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 7, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application pro grams, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 8, an illustrative cloud computing environment 50is depicted. As shown, cloud computing environment 50 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 8 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 9, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 8) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 9 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided: Hardware and software layer 60includes hardware and software components. Examples of hardwarecomponents include mainframes, in one example IBM® zSeries® systems;RISC (Reduced Instruction Set Computer) architecture based servers, inone example IBM pSeries® systems; IBM xSeries® systems; IBM BladeCenter®systems; storage devices; networks and networking components. Examplesof software components include network application server software, inone example IBM WebSphere® application server software; and databasesoftware, in one example IBM DB2® database software. (IBM, zSeries,pSeries, xSeries, BladeCenter, WebSphere, and DB2 are trademarks ofInternational Business Machines Corporation registered in manyjurisdictions worldwide).

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, the management layer 64 may provide at least one or moreof the functions described below. Resource provisioning provides dynamicprocurement of computing resources and other resources that are utilizedto perform tasks within the cloud computing environment. Metering andPricing provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and other types of useful functionality.

Having thus described one non-limiting technical environment that isrelated to the embodiments of this invention, it is now noted that oneexample of a disadvantage that is inherent in existing approaches toauto-scaling (using a single cloud environment) relates to a lack ofcost inclusion. The reduced flexibility of the single cloud approach toauto-scaling restricts a user to working with an infrastructure that ispre-designed, or that requires the user to design an infrastructure thatwould be sufficient for the user's needs. This lack of an ability to“spill” or auto-scale to another cloud, while possibly still beingserviced by an original cloud, limits the user's options. For example,the conventional approach does not allow inter-cloud cost comparisons tobe made while designing on-premise infrastructure (e.g., whenconfiguring all or a portion of the user's private cloud).

The various embodiments of this invention address a capability toauto-scale to multiple cloud providers while considering a multiplicityof user requirements. Some existing solutions may consider scaling inmultiple availability zones (of a single cloud) to balance a load, butdo not consider additional constraints defined by the user.

The embodiments of this invention provide in various and non-limitingaspects thereof a method and a structure and a system and computerprogram(s) for enabling a user to better meet the user's cloud computingrelated requirements while considering cost and other aspects. This isaccomplished at least in part by enabling the user to auto-scale ontomultiple cloud environments including, for example, multiple publiccloud providers.

The examples of the embodiments of this invention automaticallydetermine (without user intervention) when to scale virtual cloudinstances (e.g., containers that can share a single operating system andthat represent a minimal unit containing resources necessary to run anapplication, group of virtual machines (VMs) and/or logical groups ofVMs) onto which cloud resources of which cloud provider based on userpolicies. This is accomplished while considering, for example, cost,security restrictions and other constraints, in addition to, forexample, load balancing.

A container can be considered as a form of operating systemvirtualization that is more efficient than typical hardwarevirtualization. The container provides the necessary computing resourcesto run an application as if it is the only application running in theoperating system, i.e., with a guarantee of no conflicts with otherapplication containers running on the same machine. A logical group canbe considered as a collection of virtual machines that communicate withone another.

The examples of the embodiments of this invention provide for flexibleauto-scaling management that considers multiple aspects such assecurity, cost and availability, in addition to load balancing, whileautomatically determining (without user intervention) when to scaleup/down to which cloud environment of a multiplicity of cloudenvironments.

The examples of the embodiments of this invention consider multiplerequirements of user and cloud provider properties when automaticallyscaling (up or down) a user's cloud instances among different cloudenvironments. The use of the embodiments of this invention providessolutions to a number of related sub-problems: when to scale, where toscale and which resource(s) to add (or remove) when scaling.

The examples of the embodiments of this invention consider multiplefactors such as, but not limited to, cost, properties and thecapabilities of various cloud providers, as well as possibly securityaspects, performance requirements of the user and so forth (not a singlepoint of optimization). The examples of the embodiments of thisinvention beneficially extend auto-scaling into multiple cloudenvironments.

The use of the embodiments of this invention enables a user'scloud-based compute instance to be scaled up or down, and/or a user'scloud-based storage instance to be scaled up or down, and/or a user'scloud-based communication instance to be scaled up or down, as threenon-limiting examples of cloud-based resource type instances that can bescaled up or down. A reference to a user's “cloud instance” hereinshould thus be considered as encompassing any type or types of cloudresources that can be associated with a certain user and that areamenable to being scaled up or scaled down in accordance with variousembodiments of this invention.

Reference is made to FIG. 1 for showing a simplified and non-limitingsystem block diagram that is useful when discussing a general flow of amethod in accordance with embodiments of this invention. The methodassumes the presence of a least one user 100. The user 100 could be, forexample, a programmer or a data center administrator having one or moresoftware applications/programs that are amenable to being executed inwhole or in part in a cloud environment. A private cloud could beassociated with the user 100. The user 100 is bi-directionally connectedvia a network 105, such as a wired and/or a wireless network, to whatmay be referred to for convenience and not by limitation as amulti-cloud auto-scaling engine (MCASE) 110 that operates in accordancewith the embodiments of this invention. The multi-cloud auto-scalingengine 110 is bi-directionally connected for conducting communicationswith a plurality of clouds 160 (cloud 1-cloud i, where i is equal to orgreater than 2). The clouds 160 could include one or more public orprivate clouds. The cloud or clouds 160 with which the user 100 iscurrently associated and using may be considered to represent the user'scurrent cloud environment or the user's current cloud computinginstance.

The multi-cloud auto-scaling engine 110 includes a plurality ofinter-connected and cooperating sub-systems. A first sub-system is auser interface 120 that functions to interact with the user 100 to inputuser cloud-related requirements. A second sub-system is a cloud sensorand monitor sub-system 130 that is configured to query the clouds 160 toreceive cloud properties therefrom. A third sub-system is a decisionmaking unit or more simply a decision sub-system 140. The decisionsub-system 140 receives as inputs the user requirements from the userinterface sub-system 120 and the sensed cloud properties from the cloudsensor and monitor sub-system 130. The decision sub-system 140 operateson the input information to decide when to scale up or to scale down theuser's cloud environment, where the scaling should occur (which cloud orclouds) and which type of cloud resources to use in a selected cloud. Afourth sub-system is an execution unit or execution sub-system 150 thatoperates to communicate with the clouds 160 to carry out the decision(s)made by the decision sub-system 140.

The multi-cloud auto-scaling engine 110 can be implemented, withoutrestriction, as at least one of computer programs (software), ashardware in a system or systems, or as a combination of computersoftware and hardware.

It should be noted that the functionality of two or more of thedescribed sub-systems could be combined. As but one example, thefunctionality of one or both of the cloud sensor and monitor sub-system130 and the execution sub-system 150 could be integrated with thefunctionality of the decision sub-system 140 without departing from theteachings of the various non-limiting embodiments of this invention.

FIG. 2 shows the user interface sub-system 120 in greater detail andalso details the process of obtaining the user's requirements via anapplication-programming interface (API). When prompted the user 100inputs values and information associated with some number ofcloud-related requirements such as, but not limited to, budgetrequirements 220, performance preferences 240 and scaling policies 260.Additional user requirements can include, but are not limited to, areliability level preference, a location preference, a latencypreference (latency tolerance) and other cloud-related requirements. Allor at least some of these requirements can be considered by the decisionunit 140 when making decisions for when to scale up/scale down a cloudcomputing instance, where to scale the instance and what type ofresources to use in a cloud selected for up-scaling or for down-scaling.For example, the budget requirements 220 can define a constraint of theuser for establishing a cost limit regarding public cloud providers.Since the user 100 may own or otherwise be associated with a privatecloud, which could be assumed to not charge a fee or to charge a nominalfee for usage, the use of the private cloud could typically be morepreferable for a low budget user than the use of a public cloud (so longas the private cloud could satisfy other user requirements such asperformance and/or latency). It can be noted that in some embodimentsthe user 100 may not have access to, or be associated with, a privatecloud. The user's budget thus contributes to the cloud environmentdecision process in terms of cost optimization. The performancepreference 240 along with the scaling policies 260 can contribute to the“when” to scale aspect of the decision-making. For example, if the user100 defines high performance preferences for the cloud that the user 100is using in terms of keeping utilization of a CPU under some threshold,once the CPU usage for that cloud is greater than the threshold thedecision unit 140 can scale up to one or more other cloud providers 160.

In one non-limiting embodiment the scaling policies 260 may includeperformance preferences as a set of if-then rules to aid in determiningwhen to scale up/down the user's cloud environment. For example, if acertain user application begins to consume more than some threshold CPUcapacity then the scaling policy 260 may indicate to start a newinstance of the certain user application in order to accommodate theadditional CPU usage. Similarly, if the application begins to use fewerresources than some lower threshold specified by the scaling policy 260then the decision unit 140 may increase overall efficiency by scalingdown the application. This can be achieved, for example, by reducing thenumber of instances of the application. Other requirements, such as thecloud reliability level, the location preference for where the cloudprovider is physically located, the latency tolerance and so forth canalso be components of the decision process of the decision unit 140. Thelisted requirements are but a few examples of many other types ofrequirements.

There can be some hysteresis involved in this scale-up/scale-downprocess in order to accommodate fluctuations in the user'scloud-computing resource requirements. That is, if a sudden spike in auser's demand occurs for some time period T, where T is less than somethreshold period, than a scale-up operation may not be needed and maynot be triggered. The obverse is also true for some sudden and temporarydrop in demand.

FIG. 3 shows the cloud sensor and monitor sub-system 130 in greaterdetail, and illustrates the process of sensing/monitoring the propertiesof the different clouds 160. Such cloud properties can be, but are notlimited to, cost 310, reliability level 320, security level 330,performance level 340, infrastructure properties 350 and so forth. Thesevarious cloud properties can be ascertained and monitored through anagent 360 that forms a part of the cloud sensor and monitor sub-system130. Some of these properties such as cost 310 or infrastructureproperties 350 are typically publicly available and can be gatheredthrough use of APIs of the cloud providers themselves. Some other cloudproperties, such as the reliability level 320, the security level 330and/or the performance level 340, can be sensed/monitored viastatistical sensing/monitoring of the cloud providers. One suitabletechnique for accomplishing this is by considering, for example, privateand/or public (e.g., user group) knowledge of historical record(s) ofapplication deployments in a particular cloud (e.g., reliability,security, down time) and then deriving insights about such properties.By whatever technique(s) the cloud properties are identified it isassumed that the cloud sensor and monitor sub-system 130 can gather suchproperties and pass them to the decision sub-system 140.

The cloud sensor and monitor sub-system 130 is capable of monitoring theclouds 160 in real time or substantially real time in order to detectchanges in cloud properties such as, but not limited to, complete orpartial outages, communication link impairments, changes in cloudavailability, security-related issues and other cloud metrics. The cloudsensor and monitor sub-system 130 is then enabled to output theinformation obtained from the cloud monitoring to the decisionsub-system 140 where changes in cloud properties may result in anautomatic reconfiguration being made to the currently selectedclouds/cloud resources (within the constraints imposed by the userpreferences and policies).

FIG. 4 shows the decision sub-system 140. The decision sub-system 140takes as inputs the user requirements and preferences from the userinterface sub-system 120 and the sensed cloud properties from the cloudsensor and monitor sub-system 130. The decision sub-system 140 usesthese inputs to decide in scaling decision unit 420 whether a scaling upor a scaling down operation is necessary. The user's scaling policy 260can be a primary input to the scaling decision to trigger the scaling upor scaling down of a particular instance. The scaling decision operationcan be considered as the start of the multi-cloud auto-scaling process.Once a scaling operation is triggered (either up or down) then unit 440makes a decision as to where to start (on what cloud) the newapplication instance (if a scale-up is triggered) or where to stop analready running instance (if a scale-down is triggered). The decisionprocess could be accomplished in a number of ways. One suitabletechnique is to simply match the user requirements to the cloudproperties. While this approach may not yield any significant level ofoptimization it would, however, ensure that the user's requirements aresatisfied. While this direct matching approach may be satisfactory formany user applications, other approaches can also be employed. Forexample, one can define an objective function so as to minimize thespatial and/or communication path distance from the user 100 to wherethe application is to be executed (where the cloud provider isphysically located) in order to reduce data transmission latency and/oror minimize the cost of usage. The use of such an objective function canimprove the solution space.

Once the decision is made as to which cloud to use for the scale up orscale down, the next decision is made by a resource unit 460 todetermine which resources to employ in the selected cloud. The resourceunit 460 solves another optimization problem based on the given userinput from the user interface sub-system 120 and the sensed cloudproperties from the cloud sensor and monitor sub-system 130. Based onthe application properties defined by the user 100 a method executed bythe resource unit selects those resource types (e.g., CPUs, memory,communication adapters, OS, etc.) that would best meet the user'sapplication preferences when scaling up, or the method selects a leastappropriate match of resource types for the application when scalingdown. The resource unit 460 seeks to ensure an optimumresource-application pairing based on the user requirements andpreferences from the user interface sub-system 120 in view of the sensedcloud properties from the cloud sensor and monitor sub-system 130.

Once the decisions are made by the decision sub-system 140 on when toscale, where to scale and which type of cloud resources to use, theexecution sub-system 150 executes the decision by communicating with theapplicable cloud or clouds 160. If the decision is to scale up on cloudi by using resource type k, then the execution sub-system 150 createsthis new instance on the applicable cloud or clouds. Similarly, if thedecision is to remove an instance of resource type k from cloud i theexecution sub-system 150 communicates with cloud i to accomplish thescale down.

It can be noted that scaling up or scaling down can involve more thanone of the clouds 160. For example, if the resource requirements of theuser 100 increase significantly, and depending on current cloudavailabilities, it can be the case that the decision sub-system 140 willlaunch two additional instantiations of the user's application, with onebeing launched, for example, on Cloud 1 and another being launched, forexample, on Cloud 3.

What follows is a non-limiting example of the use of the multi-cloudauto-scaling engine 110. The example assumes that a certain cloud user100 is currently using cloud 1, cloud 2 (an on premise, private cloud ofthe user in this non-limiting example) and cloud 3. It is also assumedthat the certain cloud user has a user requirement vector such that: (a)Budget 220: $5/month and (b) Performance 240: none specified. The user'sscaling policy 260 is as follows: (a) scale up if the instance isconsuming 85% CPU utilization in its physical resource and (b) scaledown if the instance is consuming 20% CPU utilization in its physicalresource. A reliability level requirement specifies that a very reliabledata center is needed (which can be related to the physical location ofa selected cloud). With regard to the user's location preference, assumethat the user 100 is located in Europe and is most interested in cloudservice provider datacenters that are located primarily in the Europeanregion. With regard to the user's latency preference it can be assumedfor this example that the application that the user 100 is running doesnot require low latency and, therefore, the application may run inmultiple data centers that are physically isolated from one another. Auser demand requirement can be 2 CPU cores usage per instance, nocommunication.

These various requirements of the user 100 are collected by and outputfrom the user interface sub-system 120 and passed (communicated) to thedecision sub-system 140. In addition, the cloud sensor and monitorsub-system 130 senses, collects and possibly derives (e.g., based onhistorical usage data) the various properties of the clouds 160 andpasses them to the decision sub-system 140. The following Table 1summarizes exemplary properties of Clouds 1, 2, 3, 4, and 5 (more orless than five clouds could be accommodated by the examples of theembodiments of this invention). Note that while there are a total offive clouds to make decisions for in this non-limiting example, Cloud 2is free of charge to the user which implies that Cloud 2 is the user'sprivate cloud. In the example Cloud 2 has zero Availability, meaningthat it cannot be considered in the scale-up decision making.

TABLE 1 Different cloud provider properties Usage cost Commu- Reli-Avail- per CPU nication ability ability Cloud # core cost level Locationin CPU % Cloud 1 0.02$/core 0.01$/gbps Low Asia-Pacific 50% Cloud 2 freefree High Europe  0% Cloud 3 2.94$/core 0.31$/gbps Med West Coast 80%Cloud 4 0.05$/core 0.12$/gbps Low East Coast 100%  Cloud 5 0.75$/core0.25$/gbps High Europe 100% 

It is pointed out that the Availability property can be sub-divided intoa plurality of Availabilities such as, but not limited to, CPUAvailability, Storage Availability and Communication Availability. Thesevarious examples of Availabilities could, in some embodiments, besensed, monitored and considered separately.

In addition to sensing the input in Table 1 the cloud sensor and monitorsub-system 130 further inputs the performance of instances running onthe cloud(s). In this specific and non-limiting example the user 100 isalready running the instances on Cloud 2 (private cloud) and the cloudsensor and monitor sub-system 130 monitors the performance level of theinstance running on Cloud 2. In the example Cloud 2 has zeroAvailability, meaning that it cannot be considered in the scale-updecision making.

After the user interface sub-system 120 and the cloud sensor and monitorsub-system 130 pass their information to the decision sub-system 140 thedecision sub-system 140 begins operation. In this example once themonitored CPU level reaches 85% in Cloud 2 (CPU level is already 100%meaning the CPU Availability is 0% in Table 1), the scaling decisionunit 420 makes the decision to scale up the instance. Next, thewhere-to-scale unit 440 makes the decision on where to scale theinstance.

One technique to select the cloud that matches the user requirements isto generate a requirement satisfaction vector and select the cloud thatexhibits the highest scalar value. Such a requirement satisfactionvector can have binary values, e.g., 1 meaning the requirement issatisfied and 0 meaning the requirement is not satisfied. For example,the Budget requirement is satisfied in all clouds except Cloud 3. Theestimated usage cost for each cloud is 0.04, 0, 5.88, 0.1, 0.15 forcloud 1, 2, 3, 4 and 5 respectively. Therefore the requirementsatisfaction level for this particular requirement would be [1, 1, 0, 1,1]. Similarly, one can write the Reliability level satisfaction vectoras [0, 1, 0, 0, 1] and a Latency preference vector as [0, 1, 0, 0, 1].The Availability requirement satisfaction vector is [1, 0, 1, 1, 1]. Theuser can specify to meet the requirements “softly” (it would bepreferred, but not essential, to meet the requirement) or “hard” (mustbe satisfied). One requirement that preferably is by default alwaysspecified as hard is the Availability requirement. If the Availabilityrequirement is not satisfied then that particular cloud is notconsidered for further selection steps. Assuming that all therequirements are hard, and based on the requirement vectors, Cloud 5 isthe only cloud that satisfies all the requirements. The entry wisemultiplication of the individual requirement satisfaction vectors (knownas Hadamard products) can be helpful to find the cloud or clouds thatsatisfy the user's requirements. For this non-limiting example the entrywise multiplication is [0, 0, 0, 0, 1] therefore the Cloud 5 is the onlycloud that satisfies the user requirements. In response thewhere-to-scale unit 440 selects Cloud 5 as the scale-up target. OnceCloud 5 is selected for scaling up the instance the resource unit 460examines the available resource types in Cloud 5. For example, a cloudcan provide compute and storage instances. If the application that theuser 100 is running is primarily a compute instance then the resourceunit 460 will select the compute resource to use. The resource types anduser instance type match can be done via same method as is performed bythe where-to-scale unit 440.

Once the decision sub-system 140 makes all needed decisions theexecution sub-system 150 creates the new instance in Cloud 5 usingwhatever communication/data/interface requirements are needed by Cloud 5to instantiate a new instance. Scaling down is similar to scaling upexcept the instance that will be removed will be removed from the cloudthat currently has the least satisfactory requirement satisfactionvector.

The foregoing example is descriptive of but one specific implementation(matching user requirements via vector) of the decision sub-system 140.However, in other embodiments of this invention the decision makingprocess could be based on, as but a few non-limiting examples, linearprogramming, integer programming, and heuristic solutions.

The embodiments of this invention provide for making a rule-based,preferably optimized decision on when to scale an application instance.If optimization criteria are not given by the user 100 then obtaining afeasible solution within the user requirements may be sufficient. Thewhere-to-scale decision made by the unit 440 considers all theconditions as well as the trade-off between cost/performance. Thedecision sub-system 140 is presented with a multi-dimensional problemthat can be solved simultaneously or locally.

The multi-cloud auto-scaling engine 110 could be embodied in softwarestored in a memory of a data processing system owned or controlled bythe user 100 and executed as an application by the user's dataprocessing system. FIG. 5 shows one non-limiting example of anembodiment of a data processing system 500 suitable for hosting themulti-cloud auto-scaling engine 110. The data processing system 500includes at least one data processor or CPU 510 bidirectionallyconnected with at least one computer-readable data storage medium suchas one embodied in memory 520. The memory 520 can store computer programcode 525 configured to implement the multi-cloud auto-scaling engine 110when read from the memory 520 and executed by the CPU 510. Alsoconnected to the CPU 510 is at least one user input/output device 530(e.g., a keyboard, touch sensitive (display) surface, voice recognitiondevice, etc.,) whereby the user is enabled to enter the userrequirements to the API of the user interface 120 of the multi-cloudauto-scaling engine 110. Also connected to the CPU 510 is at least onedata communication adapter/port 540 whereby the multi-cloud auto-scalingengine 110 can gain access to the clouds 160. Depending on theimplementation the data processing system 500 can also include acommunication path 550 to a private cloud 560 (if not connected to theprivate cloud 560 via the data communication adapter/port 540).

In some embodiments of this invention the data processing system 500could be implemented with a PC or a workstation, or it could beimplemented by a computing center and server functionality associatedwith a user data center.

In some embodiments of this invention the data processing system 500could be implemented in a mobile device such as a smart phone or atablet or similar device and the multi-cloud auto-scaling engine 110could be implemented as a mobile application (mobile app) that isresident on and executed by the CPU of the mobile device. In this typeof embodiment the various communication adapters and paths 540 and 550could be implemented using wireless technology (e.g., one or more of acellular link, a wireless local area network (WLAN) link, a Bluetoothlink, etc.)

In some embodiments some or all of the computer software and relatedhardware that embodies the multi-cloud auto-scaling engine 110 could beresident in the user's private cloud 560. In this case, and for theexample of the mobile device user platform, the mobile device platformwould only need to include software that provides an API to themulti-cloud auto-scaling engine 110 in the user's private cloud.

In some embodiments some or all of the computer software and relatedhardware that embodies the multi-cloud auto-scaling engine 110 could beresident at a computing system or center of an entity that offers thefunctionality of the multi-cloud auto-scaling engine 110 as a service,such as a subscription service, whereby the user 100 is enabled bysubscription to communicate via a data communications network (wiredand/or wireless) to a local area network (LAN) or a wide area network(WAN. e.g., the Internet) to upload the user requirements and anyrelated information and to thus achieve the benefits that result fromthe use of this invention.

In general then it should thus be realized that the various embodimentsof this invention are agnostic with regard to the specifics of thetechnical implementation and location of the multi-cloud auto-scalingengine 110.

FIG. 6 shows a logic flow diagram in accordance with one (non-limiting)embodiment of this invention. At Blocks 605 and 610 the user interfacesub-system 120 and the cloud sensor and monitor sub-system 130 operatesto gather the user's requirements/objective preferences and themonitor/collect usage data for an instance. This information is passedto the decision sub-system 140 that determines at Block 615 when totrigger a scaling operation. If scaling is triggered (Block 620) adecision is made at Block 625 whether to add or drop an instance(scale-up or scale-down). When an instance is to be added, controlpasses to Block 630 to determine if the user 100 has defined anobjective preference. If the user has defined an objective preferencecontrol passes to Block 635 to solve the optimization problem forselecting one cloud provider to add the instance to (WHERE), and then toBlock 640 to solve the optimization problem for selecting whichresource(s) in the selected cloud provider for adding the instance to(WHICH). Control then passes to Block 650 where the execution sub-system150 adds the instance to the selected cloud provider using the selectedresource(s).

If it is determined at Block 630 that the user 100 has not defined anobjective preference control passes Block 655 to only find a feasiblesolution (cloud provider, WHERE). If a feasible solution cannot be foundcontrol passes to Block 665 to inform the user 100 that there is nofeasible solution at present to scaling up the instance with a cloudprovider. Assuming that a feasible solution (cloud provider) is foundthen control passes to Block 660 to find a feasible solution forselecting which resource in the selected cloud is to be used for addingthe instance to (WHICH). If no suitable resource can be found in theselected cloud control passes to Block 665 to so inform the user 100,otherwise control passes to Block 650 where the execution sub-system 150adds the instance to the selected cloud provider using the selectedresource(s).

When the decision made at Block 625 is to drop an instance controlinstead passes to Block 670 and the flow of Blocks 670-695 mirrors theflow described for Blocks 630-665 except that an instance is removed (iffeasible). More specifically, when an instance is to be dropped controlpasses to Block 670 to determine if the user 100 has defined anobjective preference. If the user has defined an objective preferencecontrol passes to Block 675 to solve the optimization problem forselecting one cloud provider from which to remove the instance (WHERE),and then to Block 680 to solve the optimization problem for selectingwhich resource(s) in the selected cloud provider for removing theinstance from (WHICH). Control then passes to Block 695 where theexecution sub-system 150 removes the instance to the selected cloudprovider using the selected resource(s). If it is determined at Block670 that the user 100 has not defined an objective preference controlpasses Block 685 to find a feasible solution (cloud provider, WHERE). Ifa feasible solution cannot be found control passes to Block 665 toinform the user 100 that there is no feasible solution at present toscaling down the instance with a cloud provider. Assuming that afeasible solution (cloud provider) is found then control passes to Block690 to find a feasible solution for selecting which resource in theselected cloud for removing the instance from (WHICH). If no suitableresource can be found in the selected cloud control passes to Block 665to so inform the user 100, otherwise control passes to Block 650 wherethe execution sub-system 150 removes the instance from the selectedcloud provider using the selected resource(s).

Based on the foregoing it should be appreciated that embodiments of thisinvention provide a method, system and computer program productconfigured for scaling virtual instances (e.g., containers, group ofVMs, logical groups, etc.) into multiple cloud environments thatmaximizes user satisfaction. The method includes obtaining userrequirements such as expected performance; cost, security, objectivepreference and so forth. The method further includes sensing/monitoringproperties of clouds such as cost, reliability, security and so forth.The method then makes decisions, based at least on the obtained userpreferences and the sensed/monitored cloud properties. The decisionsinclude when to scale, where to scale (to what cloud) and whichresources to use (in the selected cloud) for scaling up/down by somenumber of instances.

Sensing/monitoring the cloud properties can include, but are not limitedto, sensing the cost of the cloud, sensing the reliability level of thecloud, sensing the security level of the cloud, sensing theinfrastructure properties of the cloud and monitoring the performancelevel of the cloud.

Making the decisions considers the user requirements, cloud providerproperties, and objective preferences of the user and includes when toscale (up/down) based on user defined conditions such as CPU thresholdand/or response time, where to scale based on user defined requirementssuch as budget constraint and/or security conditions, and which (whattype of) resource(s) to use in different clouds, such as different VMtypes.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

As such, various modifications and adaptations may become apparent tothose skilled in the relevant arts in view of the foregoing description,when read in conjunction with the accompanying drawings and the appendedclaims. As but some examples, the use of other similar or equivalentmathematical expressions may be used by those skilled in the art.However, all such and similar modifications of the teachings of thisinvention will still fall within the scope of this invention.

What is claimed is:
 1. A method executed at least in part by a digitaldata processor, comprising: obtaining information, via a user interface,that is descriptive of user cloud computing related preferences, whereinthe user cloud computing related preferences comprises at least a usercloud computing budgetary preference, a location preference, aperformance preference, and a reliability level preference, and whereinobtaining the information further comprises obtaining indications onwhether each of the user cloud computing related preferences is eitherrequired or preferred; sensing properties of a plurality of cloudsincluding at least reliability, cost, security, location, performanceand infrastructure properties, wherein the sensed cost andinfrastructure properties are obtained directly from a cloud providervia an application program interface of the cloud provider, and whereinthe reliability, security, and performance properties are obtained atleast in part based on one or more historical records of applicationdeployments in each of the plurality of clouds; making decisions, basedat least on the obtained information and on the sensed properties of theplurality of clouds, of when to scale up or scale down a user cloudinstance, of selecting one of the plurality of clouds as where to scalethe user cloud instance, and determining which resource or resources ofthe selected cloud to add or remove from the selected cloud; where inresponse to detecting a presence of a trigger to perform a user cloudinstance scale up operation, solving a first optimization problem toselect where to scale up the user cloud instance by selecting one of theplurality of clouds to add the user cloud instance to and then solving asecond optimization problem to select which resource or resources in theselected cloud to add the user cloud instance to, where the selectedresource or resources comprise at least one of memory, communicationadapters and operating system, wherein selecting one of the plurality ofclouds to add comprises generating a requirement satisfaction vector foreach of the user cloud computing related preferences comprising binaryvalues indicating whether each of the plurality of clouds satisfy agiven one of the user cloud computing related preferences, and selectingthe cloud based on both the requirement satisfaction vectors and theobtained indications on whether each of the user cloud computing relatedpreferences is either required or preferred; and automatically executingthe decisions on the selected cloud.
 2. The method of claim 1, where theuser's cloud instance is being executed on a private cloud when thedecisions are executed, and where the selected cloud is a public cloud.3. The method of claim 1, where the obtained information descriptive ofuser cloud computing related preferences comprises a latency preference,in addition to the user cloud computing budgetary preference, locationpreference reliability level preference, and performance preference. 4.The method of claim 1, where the obtained information is furtherdescriptive of a user objective preference as to a certain cloudprovider or cloud providers.
 5. The method of claim 1, where the step ofmaking decisions is automatically triggered by at least a change in auser application instance so as to require more cloud resources or lesscloud resources.
 6. The method of claim 1, comprising in response to thepresence of the trigger initially determining whether the trigger is toscale up or scale down the user cloud instance, where in response todetermining that the trigger is to scale up the user cloud instance,determining if there exists a user-defined objective preference and, ifthe objective preference is determined to exist, then solving the firstoptimization problem and the second optimization problem.
 7. The methodof claim 6, where if it is determined that the user-defined objectivepreference does not exist, the method further comprises attempting tofind a feasible solution by selecting a suitable cloud to add the usercloud instance to and selecting a suitable resource in the selectedcloud for adding the user cloud instance to; and if no feasible solutioncan be found for selecting either a suitable cloud or a suitableresource in a selected cloud, informing the user.
 8. The method of claim1, comprising in response to the presence of the trigger initiallydetermining whether the trigger is to scale up or scale down the usercloud instance, where in response to determining that the trigger is toscale down the user cloud instance, determining if there exists auser-defined objective preference and, if the objective preference isdetermined to exist, then solving a first optimization problem to selectwhere to scale down the user cloud instance by selecting one of theplurality of clouds to remove the user cloud instance from and thensolving a second optimization problem to select which resource orresources in the selected cloud to remove the user cloud instance from.9. The method of claim 8, where if it is determined that theuser-defined objective preference does not exist, the method furthercomprises attempting to find a feasible solution by selecting a suitablecloud to remove the user cloud instance from and selecting a suitableresource in the selected cloud to remove the user cloud instance from;and if no feasible solution can be found for selecting either a suitablecloud or a suitable resource in a selected cloud, informing the user.10. A method executed at least in part by a digital data processor,comprising: obtaining information, via a user interface, that isdescriptive of user cloud computing related preferences, wherein theuser cloud computing related preferences comprises at least a user cloudcomputing budgetary preference, a location preference, a performancepreference, and a reliability level preference, and wherein obtainingthe information further comprises obtaining indications on whether eachof the user cloud computing related preferences is either required orpreferred; sensing properties of a plurality of clouds including atleast reliability, cost, security, location, performance, andinfrastructure properties, wherein the sensed cost and infrastructureproperties are obtained directly from a cloud provider via anapplication program interface of the cloud provider, and wherein thereliability, security, and performance properties are obtained at leastin part based on one or more historical records of applicationdeployments in each of the plurality of clouds; making decisions, basedat least on the obtained information and on the sensed properties of theplurality of clouds, of when to scale up or scale down a user cloudinstance, of selecting one of the plurality of clouds as where to scalethe user cloud instance, and determining which resource or resources ofthe selected cloud to add or remove from the selected cloud, whereinselecting one of the plurality of clouds to add comprises generating arequirement satisfaction vector for each of the user cloud computingrelated preferences comprising binary values indicating whether each ofthe plurality of clouds satisfy a given one of the user cloud computingrelated preferences, and selecting the cloud based on both therequirement satisfaction vectors and the obtained indications on whethereach of the user cloud computing related preferences is either requiredor preferred; and automatically executing the decisions on the selectedcloud; where in response to the presence of a trigger that isautomatically generated by at least a change in a user applicationinstance so as to require more cloud resources or less cloud resources,the method comprises initially determining whether the trigger is toscale up or scale down the user cloud instance, where in response todetermining that the trigger is to scale up the user cloud instance,determining if there exists a user-defined objective preference and, ifthe objective preference is determined to exist, the method furthercomprises solving a first optimization problem to select where to scaleup the user cloud instance by selecting one of the plurality of cloudsto add the user cloud instance to and then solving a second optimizationproblem to select which resource or resources in the selected cloud toadd the user cloud instance to.
 11. The method of claim 10, where theuser's cloud instance is being executed on a private cloud when thedecisions are executed, and where the selected cloud is a public cloud.12. The method of claim 10, where the obtained information descriptiveof user cloud computing related preferences comprises latencypreference, in addition to the user cloud computing budgetarypreference, location preference and reliability level preference andperformance preference.
 13. The method of claim 10, where the obtainedinformation is further descriptive of the user objective preference asto a certain cloud provider or cloud providers.
 14. The method of claim10, where if it is determined that the user-defined objective preferencedoes not exist, the method further comprises attempting to find afeasible solution by selecting a suitable cloud to add the user cloudinstance to and selecting a suitable resource in the selected cloud foradding the user cloud instance to; and if no feasible solution can befound for selecting either a suitable cloud or a suitable resource in aselected cloud, informing the user.
 15. The method of claim 10, wherethe selected resource or resources comprise at least one of memory,communication adapters and operating system.